The Biostat
Types of Linear Correlation
Pearson's Correlation Coefficient (r)
Pearson's R measures the strength or degree of association between two interval ratio variables ranging from .0 to 1 either positive or negative. It is the square root of correlation determination. The closer the measure is to 1 or -1, the stronger the relationship. Thus, 80 or 90 in either direction indicates a strong relationship exists. Zero means there is no correlation. Pearson's R is the most commonly used correlation measure. It uses the following formula:
R = covariance/(standard deviation x)(standard deviation y). Pearson’s Coefficient of correlation
The most important algebraic method of measuring correlation is Karl Pearson’s Coefficient of correlation or Pearsonian’s coefficient of Correlation. It has widely used application in Statistics. It is denoted by r.
The formula is given by
r = n∑xy−∑x∑yn∑x2−(∑x)2√n∑y2−(∑y)2√
Interpretation of Karl Pearson’s Coefficient of correlation
Karl Pearson’s Coefficient of correlation denoted by r is the degree of correlation between two variables. r takes values between –1 and 1
When r is –1, we say there is perfect negative correlation.
When r is a value between –1 and 0, we say that there is a negative correlation
When r is 0, we say there is no correlation
When r is a value between 0 and 1, we say there is a positive correlation
When r is 1, we say there is a perfect positive correlation.
Properties of the Coefficient of correlation
1. Coefficient of correlation has a well defined formula
2. Coefficient of correlation is a number and is independent of the unit of measurement
3. Coefficient of correlation lies between –1 and 1
4. Coefficient of correlation between x and y will be same as that between y and x.
Correlation Determination
Correlation determination measures the proportional reduction error resulting from linear regression. According to the text "Social Statistics for a Diverse Society,"
Pearson's Correlation Coefficient (r)
Pearson's R measures the strength or degree of association between two interval ratio variables ranging from .0 to 1 either positive or negative. It is the square root of correlation determination. The closer the measure is to 1 or -1, the stronger the relationship. Thus, 80 or 90 in either direction indicates a strong relationship exists. Zero means there is no correlation. Pearson's R is the most commonly used correlation measure. It uses the following formula:
R = covariance/(standard deviation x)(standard deviation y). Pearson’s Coefficient of correlation
The most important algebraic method of measuring correlation is Karl Pearson’s Coefficient of correlation or Pearsonian’s coefficient of Correlation. It has widely used application in Statistics. It is denoted by r.
The formula is given by
r = n∑xy−∑x∑yn∑x2−(∑x)2√n∑y2−(∑y)2√
Interpretation of Karl Pearson’s Coefficient of correlation
Karl Pearson’s Coefficient of correlation denoted by r is the degree of correlation between two variables. r takes values between –1 and 1
When r is –1, we say there is perfect negative correlation.
When r is a value between –1 and 0, we say that there is a negative correlation
When r is 0, we say there is no correlation
When r is a value between 0 and 1, we say there is a positive correlation
When r is 1, we say there is a perfect positive correlation.
Properties of the Coefficient of correlation
1. Coefficient of correlation has a well defined formula
2. Coefficient of correlation is a number and is independent of the unit of measurement
3. Coefficient of correlation lies between –1 and 1
4. Coefficient of correlation between x and y will be same as that between y and x.
Correlation Determination
Correlation determination measures the proportional reduction error resulting from linear regression. According to the text "Social Statistics for a Diverse Society,"